DEvS: Data Distillation Algorithm Based on Evolution Strategy

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dc.contributor.authorShvai, Nadiya
dc.contributor.authorLlanza, Arcadi
dc.contributor.authorHasnat, Abul
dc.contributor.authorNakib, Amir
dc.date.accessioned2022-11-04T07:29:17Z
dc.date.available2022-11-04T07:29:17Z
dc.date.issued2022
dc.description.abstractThe development of machine learning solutions often relies on training using large labeled datasets. This raises challenges in terms of data storage, data privacy protection, and longer model training time. One of the possible solutions to overcome these problems is called dataset distillation – a process of creating a smaller dataset while maximizing the preservation of its task-related information. In this paper, a new dataset distillation algorithm is proposed, called DEvS, which uses an evolutionary strategy approach to condense the training samples initially available for an image classification task, while minimizing the loss of classification accuracy. Experiments on CIFAR-10 demonstrate the competitiveness of the proposed approach. Also, contrary to recent trends, DEvS is derivative-free image generation, and therefore has greater scalability on larger input image sizes.en_US
dc.identifier.citationDEvS: Data Distillation Algorithm Based on Evolution Strategy / Shvai Nadiya, Llanza Arcadi, Hasnat Abul, Nakib Amir // Proceedings of the Genetic and Evolutionary Computation Conference Companion, 9-13 July 2022. - 2022. - P. 292-295. - https://doi.org/10.1145/3520304.3528819en_US
dc.identifier.urihttps://doi.org/10.1145/3520304.3528819
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/23979
dc.language.isoenuk_UA
dc.relation.sourceProceedings of the Genetic and Evolutionary Computation Conference Companion, 9-13 July 2022en_US
dc.statusfirst publisheduk_UA
dc.subjectdataset distillationen_US
dc.subjectimage classificationen_US
dc.subjectneural networksen_US
dc.subjectevolution strategyen_US
dc.subjectoptimizationen_US
dc.subjectconference materialsen_US
dc.titleDEvS: Data Distillation Algorithm Based on Evolution Strategyen_US
dc.typeConference materialsuk_UA
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